Decisions can solve problems or create opportunities, and businesses depend on the right decisions at the right time. Invariant operations may use intuitive or rule-of-thumb methods for decision making. Changing conditions make decisions non-trivial because consequences of simple actions are compounded by the complex relationships of amounts and timing between resources. Methods like the Critical Path Method (CPM) use graphical representations to show sequence, dependency, and constraints. The Project Evaluation and Review Technique (PERT) uses task dependencies and status relative to forecasted completion to revise schedules and coordinate tasks.
Prospective scheduling methods solve a wide variety of business resource management problems. Resources may include raw materials, machine time, labor, space, power, or any other entity whose constraint affects the delivery of goods or services. In construction, the scheduled use of resources includes people with general or specialized skills, various single task or multi-purpose equipment, and materials requirements; in transportation, the efficient use of special or general purpose vehicles to transport people, materials, and equipment between a multitude of locations; in health care, the scheduled use of beds, operating rooms, general or specialized staff, and fixed or mobile equipment.
A response to changing requirements or constraining conditions should consider the inter-dependency of resources to correctly determine present and future resource impacts. The automation of prospective scheduling is unique in its ability to transform a simple list of requirements, dependencies, and constraints into a "mountain of data" describing future resource utilization amounts and timing. Data volumes increase dramatically with product and processes complexity. The creation of detailed schedules is a non-trivial task, but finding useful information has become the most difficult problem. Data may be simplified by approximating scheduled events to the nearest day or week (called "bucketing") and by using simple models to represent resource characteristics and dependency. Schedules can be simplified but realism is lost.
Prospective scheduling identifies where and when resource magnitude or timing constraints will be violated so these conflicts may be resolved before they actually happen. Relationships and constraints associated with products and processes must be accurately modeled to predict future events accurately. Models can include process yield and probability factors, but can not predict random events such as equipment failure, missing parts, or bad weather. Random events must be considered so that material and capacity "buffers" can be used to prevent bottlenecks. The use of "substitute" resources can also prevent bottlenecks. In either case, timely recognition and response (scheduling around) is essential to maintained productivity, and timely precise resource schedules are often a critical success factor. Material Requirements Planning (MRP) determined future material requirements and potential shortages to reduce inventory and minimize material disruptions. Accurate inventory, Bill Of Materials (BOM), and status information was required. MRP functioned well with invariant requirements (build to forecast), infrequent design changes, and a fixed production process segregated by product.
Increased manufacturing flexibility created a need for long term strategies based on production capacity and anticipated product mix. Similar scheduling methods were applied to control business operations including production capacity, distribution, equipment maintenance, and shop floor scheduling. Frequent adjustments to schedules were required because small changes in requirements, status, products, processes, or their constraints result in changes to many resources.
Manufacturing Resource Planning (MRP-II) divided enterprise resource management into smaller manageable pieces managing material and capacity requirements independently and interactively, developing schedules for dependent resources based on simple lead-time constraints or limited finite models. The common practice of converting external real dates to internal contiguous shop dates using a shop calendar simplified lead-time offset calculation but restricted the use of multiple calendars in finite planning and prevented net-changes to calendars. Dependent resource amounts and timing were approximated. Timing of variable size lots could not be accurately determined from conventional routings.
Prior art has not provided an integrated solution for different levels of resource management. The MRP-II model divides plans into "levels" in order to separate strategic, tactical, and operation plans because no one set of rules would allow practical co-existence. Operational resource utilization rules would consider details not relevant at the strategic level, producing detailed schedules by shift for the next two or more years. Large data volumes would make these schedules unmanageable both by the system and user. Conversely, operational plans produced by strategic planning rules would summarize plans in monthly or quarterly periods, with no visibility of detailed daily requirements.
MRP-II was designed to create stable production schedules to drive manufacturing operations. MRP-II was NOT intended for decision support or to respond to changing conditions. A limitation of this modular method is lack of visibility between strategic, tactical, and operational levels of resource management. Each level redefines resource schedules over different planning horizons and resolution, so that relationships between levels is often confusing. In practice each planning module may be provided by a different vendor, and no standard for inter-operability exists. This makes it difficult to determine future or global impacts of short term random events, such as equipment failure, and to respond quickly and optimally. When problems exist, but alternatives can not be identified or compared, predicted resource conflicts are not resolved and productivity is lost.
The Optimized Production Technology (OPT) recognized that flexible manufacturing could not be controlled by local strategies, in direct conflict with MRP-II approach of dividing resource management into specialized modules. OPT understood the role of single constraints (bottlenecks) in determining production throughput and efficiency. MRP-II implementations often ignored the effects of interacting resources so that global optimums are not achieved. The MRP-II strategy of "follow the schedule" pushed orders through production while failing to anticipate future scheduling bottlenecks. Orders could be "pushed" through production at a rate greater than the overall process can accommodate.
OPT methods can provide high production efficiency, but time consuming calculations have limited its use to production process design and monthly or quarterly adjustments. Many OPT principles were adopted in Just-In-Time (JIT) manufacturing. JIT does not anticipate resource requirements and does not require calculation. Instead, JIT schedules according to bottlenecks by pulling orders through production, avoiding queue backup preceding bottleneck resources. JIT relies on MRP-II to develop prospective material and strategic resource schedules.
Advances in the speed and flexibility of manufacturing equipment and methods have provided responsiveness that prior art prospective methods can not control. Robotics, Computer Integrated Manufacturing (CIM), flexible work cells, and methods like JIT have compressed production times from weeks to hours. The ability to produce a variety of products through the same production facilities creates bottlenecks that constantly move. Manufacturing methods have created business opportunities and competitive advantages by reducing customer delivery time and costs. These methods depend on prudent decisions to optimize productivity and delivery in a constantly changing environment.
Prior art methods for net-change updating is a discrete (batch) process that revises schedules overnight, providing schedules and exceptions each morning that reflects the real conditions (reality) at the end of the previous day. Soon after the new day begins, a decision to correct just one of hundreds of exceptions partially invalidates any remaining exceptions. A single change in production policy could affect hundreds or thousands of schedules. Each day when machine availability, customer requirements, priorities, etc. change, schedules and exceptions no longer reflect reality. Schedules are often days or weeks behind real world events they represent.
Scheduling exceptions determined by nightly processing identify future problems based on current (steady state) conditions. Real world conditions are NOT steady state, and even when schedules remain unchanged their condition can change with the passage of time. These situations result in unrealistic information and poor decisions.
Discrete methods fail to recognize changes in conditions, constraints, and dependent relationships as they occur. When schedule maintenance is collected and processed as a batch, any single change ignores its interaction with other changes. Resource dependencies create hidden relationships between exceptions and their corrective actions. Ignoring these interactions can produce more exceptions than before, a common behavior of the prior art. Discrete methods delay schedule updates and the probability of correct schedule changes. Timeliness is further compromised when change activity is unusually high. Daily schedule "adjustments" are only practical to a small proportion of schedules, and require "locking" data during processing to assure update integrity. The processing of net-changes is far less efficient than the initial creation of all the schedules. Powerful parallel computing platforms can accelerate processing, but operation is still discrete and methods are poorly suited for the tasks required.